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01. What are the different types of data available in an organization? 1 Click
02. What data should constitute master data? 2 Click
03. What is Master Data Management (MDM)? 2 Click
04. What are the key characteristics of MDM system? 3 Click
05. What is MDM hub? 3 Click
06. What are the different architecture styles of MDM hub systems? 5 Click
07. What are the key benefits of implementing MDM? 7 Click
08. What is the difference between data warehouse and MDM? 7 Click
09. How can data warehouse systems leverage MDM? 9 Click
10. What are the challenges in implementing MDM? 9 Click
11. Do leading Vendors support MDM? 10 Click
12. Can MDM be implemented with Open Source? 11 Click

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Data available in an organization can be classified into five types: Master data are the core elements of the business that are applied to multiple
transactions and are used to categorize, aggregate, and evaluate the
transaction data. Master data is not transaction data but is almost always
involved with transactional data. For example: Consider a single transaction
"John Doe sold one laser printer, product number MX0315, from Scanners &
Printers product family for $90 on 12th December 2011". Master data
elements in this transaction are Salesperson (John Doe), Product Group
(Scanners & Printers), Product (Laser Printer) and Product Number
(MX0315). It can be observed that Master data is synonymous to Dimensions
in a data warehouse.
What data should constitute master data?What are the different types of data available in an organization?1 2
Unstructured – Data that cannot be organized into identifiable structure. E.g.
emails, web pages, word processor documents etc.
Transactional – Data that forms the transactions processed by the operational
systems of the enterprise, e.g. sales, trades, etc. Transactional data typically
describes the activities or transactions of the business. Transactional data
typically captures the verbs, such as sale, delivery, purchase, email, and
revocation.
Metadata – Data that describes the data held in the enterprise information
architecture, e.g. definitions of tables and columns in the system catalog of a
database, or entities and attributes in a data model.
Hierarchical – Stores the relationships between data such as organizational
hierarchy, product lines etc.
Master – Master data are the critical nouns of a business and fall generally
into four groupings: people, things, places, and concepts. It should be the
single trusted source of data that everyone in an enterprise relies on and uses.
MDM is a set of tools, technology and processes required to create and
maintain master data. MDM ensures that an organization's critical information
(customers, vendors, products, employees, locations) is uniquely identified,
accurately defined and consistently applied across that organization's
operational systems – spanning geographic, line-of-business, and application
silo boundaries.
What is Master Data Management (MDM)?3

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A MDM system typically enables:
The MDM hub is a database with the software to manage the master data that
is stored in the database and keep it synchronized with the transactional
and/or analytical systems that use the master data.
What are the key characteristics of MDM system?4
What is MDM hub?5
Data Governance – It should provide robust security for the underlying
models, define data governance policies and procedures, and support
workflows to implement data governance policies.
Metadata Management – MDM system should have the ability to manage
business & process metadata.
Data Repository – MDM system should have the ability to model entities,
attributes, complex hierarchies and relationships among the entities.
Data Integration – MDM system should integrate with both source and
subscribing systems, ideally in both batch and real-time modes. It should
support system of entry and system of record operations.
Data Quality – MDM system should have high data quality processes
supporting standardization, de-duplication, match and merge etc.
Fig 1: MDM Hub Source: Microsoft
MDM hub contains tools and functions required to maintain master data.
Data Quality
Stewards hip and
Governance
Entity Version
Control
Workflow
Hierarchy Version
Control
Entity Management Hierarchy Management
Metadata Store
MDM Hub
ERP SharePoint HR FinanceCRM
Web Services
Master Data
Syncronization

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There are three basic architectures available namely Repository, Registry and
Hybrid.
master entity in the MDM hub, so that a significant number of MDM queries
can be satisfied directly from the hub database, and only queries that refer-
ence less-common attributes have to reference the
application database.
Gartner has identified four different implementation styles for MDM based on
criteria like authoring sour ce, data persistence or storage, data latency etc.
This is depicted below:
What are the different architecture styles of MDM hub systems?6
Repository Model
Master data for an enterprise is stored in a single database. The repository
data model must include all the attributes required by all the applications that
use the master data. The applications that consume, create, or maintain
master data are all modified to use the master data in the hub, instead of the
master data previously maintained in the application database. For example,
the Order Entry and CRM applications would be modified to use the same set
of customer tables in the master-data hub, instead of their own data stores.
Registry Model
Registry model is opposite of repository model. Master data is maintained in
the application databases, and the MDM hub contains lists of keys that can be
used to find all the related records for a particular master-data item. None of
the master-data records is stored in the MDM hub. For example, if there are
records for a particular customer in the CRM, Order Entry, and Customer Ser-
vice databases, the MDM hub would contain a mapping of the keys for these
three records to a common key.
Hybrid Model
As the name implies, the hybrid model includes features of both the repository
and registry models. The hybrid model leaves the master-data records in the
application databases and maintains keys in the MDM hub, as the registry
model does. But it also replicates the most important attributes for each
Source: Gartner
Consolidation Registry Centralized Coexistence
Author
Persistence
Validation
Master Data
authored in
Operational
Systems
Author in
distributed
systems
Hub Stores index
of master data
Hub is System
of Reference
Operational and
Analytical
Hub stores a
copy apart from
author
Hub is System
of Refrence
Downstream
Analytics and
Reporting
Author in Hub
Hub persists
master data,
copies exist in
edges
Hub is System
of Record
Author anywhere
Persist anywhere
System of Refer-
ence / Record
Upstream
Operations
Batch to real
time
Batch to event
driven
Real time Publish / Subscribe,
event-driven
Upstream
Operations
Data Latency
Consumer of
Master Data

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Organizations can achieve number of benefits including:
What are the key benefits of implementing MDM?7
• Improves data sharing and reuse.
• Eliminate redundant data management and integration activities.
• Improve product and customer management.
• Promote consistent use of data.
• Reduce supplier on boarding cost.
• Improved operational efficiency due to streamlining business
processes and good data quality.
• Refined fraud prevention.
• Improved decision making.
• Improved quality and compliance.
Characteristics Datawarehouse MDM
Provide analytical
capabilities to analyze
data across dimen-
sions.
Create and maintain a
single, consistent version
of reference data only.
Goal
Data Datawarehouse
contains transactional
(Facts) and Dimen-
sional data including
any associated hierar-
chies.
MDM contains only refer-
ence data along with any
associated hierarchies or
relationships, typically
corresponding to dimen-
sions in a data warehouse.
End user
presentation /
Touch points with
business users
Data Write back
Reports and / or
dashboards are
primarily used to
present data to end
users.
The touch points with
business users revolve
around data governance.
Emphasis is on maintaining
data quality, governance
and compliance.
Data write back to
source system is not
supported
A master data system can
write back data / provide
golden copy of data to
source system to ensure
consistency.
Although by definition both look similar and complement each other, they are
not. Fundamental difference between data warehouse and MDM are:
What is the difference between data warehouse and MDM?8

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• Integration with existing applications could provide a challenge unless
common formats are defined to exchange data across disparate
applications.
• Differing code sets, identifiers for master data across systems
provides a challenge in defining a unique identifier unless an appro
priate global standard exists that can be adopted.
• Existing business processes and timelines for creation of reference
data could be different across systems and based on the needs of
those systems. This could be a challenge when trying to implement a
centralized process for data governance if the MDM hub is used to
author master data as data synchronization issues could arise.
How can data warehouse systems leverage MDM?9
Managing Dimensional table becomes easier if dimensions in a
datawarehouse are modeled based on master data. Due to its design, master
data consists of business entities which are nothing but dimensions in
datawarehouse parlance. ETL work to load dimension data will be greatly
reduced if they are drawn from master data. Also, data enters MDM system
only if all business rules were fulfilled. This ensures that dimension data is of
high quality in data warehouse.
Most of the well-established technology vendors are providing MDM solutions
as part of their product and solution offerings. Some of the notable ones that
figure in the Gartner Magic Quadrant are given below:
IBM – IBM has three products for MDM – Infosphere MDM Server, Infosphere
MDM Server for Product Information Management and Initiate Master Data
Service. IBM has announced a convergence roadmap to integrate the prod-
ucts in a phased manner, starting with Infosphere Master Data Management
v10.0.
Informatica – Informatica acquired the former Siperian Hub and has made it
available in its portfolio as the platform for multi domain MDM.
Do leading Vendors support MDM?11
Some of the challenges in implementing MDM are given below:
What are the challenges in implementing MDM?10
• As an MDM program requires changes to existing / setting up of new
data governance processes, there would be a challenge in gaining
acceptance and support for the program, unless backed by a strong
Business Change program.
• An MDM program is not limited to its implementation as a one-time
activity. It requires to be run as a continuous program to ensure that
the data governance processes remain relevant and efficient with the
passage of time and that the master data is consistently used by
existing applications and new applications. This requires a contin-
uous commitment from management.

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Oracle – Oracle has 3 products as part of its MDM portfolio – Oracle Fusion
Customer Hub, Oracle CDH and Oracle Siebel UCM.
Can MDM be implemented with Open Source?12
Not many Open Source vendors exist in the MDM space. Talend, a global
company operating in the open source data integration space, has introduced
an open source MDM as part of its portfolio. The product comes in 2 editions
– a community edition that is available at no charge and a licensed Enterprise
edition.
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